ArticleBMC medical ethics2020
A qualitative study of big data and the opioid epidemic: recommendations for data governance.
Article in BMC medical ethics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- On the estimation of population size-A comparison of capture-recapture and multiplier-benchmark methods.Statistical methods in medical research · 2024Article
- Public Involvement and Engagement in Big Data Research: Scoping Review.Journal of participatory medicine · 2024Article
- Benefits and challenges of geographic information systems (GIS) for data-driven outreach in black communities experiencing overdose disparities: results of a stakeholder focus group.BMC public health · 2024Article
- Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach.JMIR human factors · 2024Article
- Article
- Use of Big Data and Ethical Issues for Populations With Substance Use Disorder.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2023Article
- Participatory evaluation of the process of co-producing resources for the public on data science and artificial intelligence.Research involvement and engagement · 2023Article
- Building public trust and confidence in secondary use of health data for healthcare improvement and research: a qualitative study pre-protocol.HRB open research · 2023Article
- Commentary on Adams et al.: using administrative big data for the public good.Addiction (Abingdon, England) · 2022Article
- Advancing a Data Justice Framework for Public Health Surveillance.AJOB empirical bioethicsArticle
- Visual Analytics for Data-Driven Understanding of the Substance Use Disorder Epidemic.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms.
objectivesWe aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical ways.
methodsWe conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health.
resultsConcerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches.
conclusionsUsing big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.